kmed: Distance-Based K-Medoids

Algorithms of distance-based k-medoids clustering:
simple and fast k-medoids, ranked k-medoids, and increasing number
of clusters in k-medoids. Calculate distances for mixed variable
data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and
Ahmad-Dey. Cluster validations apply internal and relative
criteria. The internal criteria include silhouette index and
shadow values. The relative criterium applies bootstrap procedure
producing a heatmap with a flexible reordering matrix algorithm
such as ward, complete, or centroid linkages. The cluster result
can be plotted in a marked barplot or pca biplot.